Please use this identifier to cite or link to this item:
|Title:||Modelling of an ICS solar water heater using artificial neural networks and TRNSYS||Authors:||Souliotis, Manolis
Kalogirou, Soteris A.
|Keywords:||Solar water heaters;Integrated Collector Storage (ICS) system;Artificial Neural Networks (ANN);TRNSYS||Category:||Environmental Engineering||Field:||Engineering and Technology||Issue Date:||2009||Publisher:||Elsevier B. V.||Source:||Renewable Energy, Vol. 34, No. 5, 2009, pp. 1333-1339||Abstract:||A study, in which a suitable artificial neural network (ANN) and TRNSYS are combined in order to predict the performance of an Integrated Collector Storage (ICS) prototype, is presented. Experimental data that have been collected from outdoor tests of an ICS solar water heater with cylindrical water storage tank inside a CPC reflector trough were used to train the ANN. The ANN is then used through the Excel interface (Type 62) in TRNSYS to model the annual performance of the system by running the model with the values of a typical meteorological year for Athens, Greece. In this way the specific capabilities of both approaches are combined, i.e., use of the radiation processing and modelling power of TRNSYS together with the “black box” modelling approach of ANNs. The details of the calculation steps of both methods that aim to perform an accurate prediction of the system performance are presented and it is shown that this new method can be used effectively for such predictions.||URI:||http://ktisis.cut.ac.cy/handle/10488/66||ISSN:||0960-1481||DOI:||http://dx.doi.org/10.1016/j.renene.2008.09.007||Rights:||Copyright © 2008 Elsevier Ltd All rights reserved.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
checked on Nov 16, 2017
Page view(s) 5036
checked on Nov 22, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.